Prognostics and health management (PHM) systems utilize a predictive learning model, such as a Neural Network, Bayesian Network, Support Vector Machine, or the like, to make predictions about a future state of an item, such as a component, sub-assembly, machine, or the like. The predicted future state may be when the item next needs maintenance, rebuilding, or replacement, for example. A PHM system may replace, or supplement, a time-based regime in which components undergo maintenance activities on a set schedule. Under such a regime, if a maintenance action is performed when it is not needed, ongoing operating costs are unnecessarily increased. By knowing which components are in need of maintenance and how soon such maintenance is required, maintenance actions can be planned in advance, while still limiting the amount of maintenance to only what is required based on the condition of the component. Among other advantages, a PHM system can provide improvement in asset availability and reduced maintenance costs and support an improved ability to plan for maintenance events.
A PHM system utilizes underlying PHM analytics and algorithms that intelligently process sensor data during operation of the machine. Generally, a PHM system includes processing raw sensor data that identifies real-time characteristics of components, training a predictive learning model based at least in part on the sensor data, and subsequently processing real-time sensor data with the predictive learning model to make predictions about future events, such as remaining useful life (RUL), time to maintenance, and the like.
Typically, the PHM system is a combination of hardware components and interrelated complex software components. Altering any aspects of the PHM analytics, such as altering the inputs to the predictive learning model, or the predictive learning model itself, requires modification of the PHM software, recompilation and debugging of the PHM software, downloading the PHM software to the machine, and re-booting the machine to implement the new PHM software. Modifying the PHM software requires a relatively skilled software technician who is knowledgeable in both the particular computer language in which the PHM software is written as well as the particular PHM software being modified, so that the modification does not result in unexpected problems. Such skilled technicians may be rare, resulting in desired modifications of the PHM analytics being delayed. Often, the user of the PHM system may have no capability of modifying the PHM system and, thus, must wait for the manufacturer of the PHM system to schedule the modification.
After the modification has been successfully implemented and tested, rolling out the modification to fleets of vehicles may be very time-consuming, and disruptive, such that each PHM system must be shut down, loaded with new PHM software, and then rebooted. This may be impractical in certain environments, such as a war zone.